A predictive method for workload forecasting in the cloud environment

Abstract

Cloud computing provides powerful computing capabilities, and supplies users with a flexible pay mechanism, which makes the cloud more convenient. People are getting more and more usage of the cloud environment due to a steady increase of data. In order to improve the performance and energy saving of the cloud computing, the efficiency of resource allocation has become an important issue. In this study, a neural network model with learning algorithm is applied to predict the workload of the cloud server. The resource manager deployed on the cloud server provides the service of managing the jobs with a resource allocation algorithm. With this prediction mechanism, cloud service providers can forecast the following time workload of cloud servers in advance. The experimental results show that resources can be allocated efficiently and become load balanced by proposed mechanism. Therefore, the cloud server can avoid the problem of inadequate resources. © Springer Science+Business Media Dordrecht 2014.